Purpose
The purpose of this study was to evaluate the diagnostic performance of single-parameter,
unimodal and bimodal magnetic resonance imaging (MRI) in differentiating tumor recurrence
(TR) from radiation necrosis (RN) in patients with glioblastoma (GBM) after treatment
using diffusion-weighted imaging (DWI), diffusion tensor imaging (DTI), dynamic susceptibility
contrast enhancement-perfusion weighted imaging (DSC-PWI), and proton magnetic resonance
spectroscopy (1H-MRS).
Materials and Methods
Patients with histologically proven GBM who underwent surgical intervention followed
by chemoradiotherapy and developed a new, progressively enhanced lesion on follow-up
MRI were included in our study. Subsequently, DWI, DTI, DSC-PWI, and 1H-MRS were performed. Then, these patients underwent a second surgical operation or
follow-up MRI to prove TR or RN. MRI metrics include apparent diffusion coefficient
(ADC) and relative ADC (rADC) values derived from DWI; fractional anisotropy (FA),
axial diffusion coefficient (DA) and radial diffusion coefficient (DR) values derived
from DTI; and relative cerebral blood volume (rCBV) and relative cerebral blood flow
(rCBF) derived from DSC-PWI. Spectral metabolites such as choline (Cho), creatine
(Cr), N-acetylaspartate (NAA), lactate (Lac), and lipids (Lip) were derived from MRS,
and the ratios of these metabolites were calculated, including Cho/NAA, Cho/Cr, NAA/Cr,
Lac/Cr, and Lip/Cr. These indices were compared between the TR group and RN group,
and the receiver operating characteristic (ROC) curve was used to evaluate the performance
in distinguishing TR from RN by using single-parameter, unimodal and bimodal MRI.
Results
There were significant differences between the TR and RN groups in terms of ADC (p = 0.001), rADC (p < 0.001), FA (p = 0.001), DA (p = 0.003), DR (p = 0.003), rCBV (p < 0.001), rCBF (p < 0.001), Cho/NAA (p < 0.001), Lac/Cr (p < 0.001) and Lip/Cr (p < 0.001). ROC analysis suggested that rCBV, MRS, and DSC + MRS were the optimal single-parameter,
unimodal, and bimodal MRI classifiers for distinguishing TR from RN, with AUC values
of 0.909, 0.940, and 0.994, respectively.
Conclusion
The combination of parameters based on multiparametric MRI in the region of enhanced
lesions is a valuable noninvasive tool for discriminating TR from RN.
Key Words
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Article Info
Publication History
Published online: December 09, 2021
Accepted:
November 10,
2021
Received in revised form:
November 6,
2021
Received:
September 21,
2021
Identification
Copyright
© 2021 The Association of University Radiologists. Published by Elsevier Inc. All rights reserved.